IDEAS home Printed from https://ideas.repec.org/p/bde/opaper/1703.html
   My bibliography  Save this paper

A suite of inflation forecasting models

Author

Listed:
  • Luis J. Álvarez

    (Banco de España)

  • Isabel Sánchez

    (Banco de España)

Abstract

This paper describes the econometric models used by the Banco de España to monitor consumer price inflation and forecast its future trends. The strategy followed heavily relies on the results from a set of econometric models, supplemented by expert judgment. We consider three different types of approaches and highlight the relevance of heterogeneity in price-setting behaviour and the importance of using models that allow for a slowly evolving local mean when forecasting inflation.

Suggested Citation

  • Luis J. Álvarez & Isabel Sánchez, 2017. "A suite of inflation forecasting models," Occasional Papers 1703, Banco de España.
    • Handle: RePEc:bde:opaper:1703
    as

    Download full text from publisher

    File URL: http://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosOcasionales/17/Fich/do1703e.pdf
    File Function: First version, February 2017
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joseph McGillicuddy & Lowell R. Ricketts, 2015. "Is Inflation Running Hot or Cold?," Economic Synopses, Federal Reserve Bank of St. Louis, issue 16.
    2. Luis J. Álvarez & Emmanuel Dhyne & Marco Hoeberichts & Claudia Kwapil & Hervé Le Bihan & Patrick Lünnemann & Fernando Martins & Roberto Sabbatini & Harald Stahl & Philip Vermeulen & Jouko Vilmunen, 2006. "Sticky Prices in the Euro Area: A Summary of New Micro-Evidence," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 575-584, 04-05.
    3. Mario Izquierdo & Juan Francisco Jimeno, 2015. "Employment, wage and price reactions to the crisis in spain: firm-level evidence from the wdn survey," Occasional Papers 1503, Banco de España.
    4. Matheson, Troy & Stavrev, Emil, 2013. "The Great Recession and the inflation puzzle," Economics Letters, Elsevier, vol. 120(3), pages 468-472.
    5. David F. Hendry & Kirstin Hubrich, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 216-227, April.
    6. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    7. Marianna Riggi & Fabrizio Venditti, 2015. "Failing to Forecast Low Inflation and Phillips Curve Instability: A Euro-Area Perspective," International Finance, Wiley Blackwell, vol. 18(1), pages 47-68, March.
    8. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    9. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    10. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    11. Jonathan H. Wright, 2013. "Evaluating Real‐Time Var Forecasts With An Informative Democratic Prior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 762-776, August.
    12. Álvarez, Luis J. & Hurtado, Samuel & Sánchez, Isabel & Thomas, Carlos, 2011. "The impact of oil price changes on Spanish and euro area consumer price inflation," Economic Modelling, Elsevier, vol. 28(1), pages 422-431.
    13. Luis Julián Álvarez & Alberto Urtasun, 2013. "Variation in the cyclical sensitivity of Spanish inflation: an initial approximation," Economic Bulletin, Banco de España, issue JUL, pages 11-17, July-Augu.
    14. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    15. Luis Julián Álvarez & Alberto Cabrero & Alberto Urtasun, 2014. "A procedure for short-term GDP forecasting," Economic Bulletin, Banco de España, issue OCT, pages 29-35, October.
    16. James H. Stock & Mark W. Watson, 2010. "Modeling inflation after the crisis," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 173-220.
    17. Sharon Kozicki & P. A. Tinsley, 2012. "Effective Use of Survey Information in Estimating the Evolution of Expected Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 145-169, February.
    18. repec:zbw:bofrdp:2014_031 is not listed on IDEAS
    19. repec:bde:journl:v:07-08:y:2013:p:09 is not listed on IDEAS
    20. Luis Julián Álvarez & Ana Gómez Loscos & Alberto Urtasun, 2015. "Asymmetries in the relationship between inflation and activity," Economic Bulletin, Banco de España, issue NOV, pages 3-9, November.
    21. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
    22. repec:bde:journl:v:11:y:2015:p:11 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nadiia Shapovalenko, 2021. "A Suite of Models for CPI Forecasting," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 252, pages 4-36.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Álvarez, Luis J. & Sánchez, Isabel, 2019. "Inflation projections for monetary policy decision making," Journal of Policy Modeling, Elsevier, vol. 41(4), pages 568-585.
    2. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    3. Ivan Kitov & Oleg Kitov, 2013. "Does Banque de France control inflation and unemployment?," Papers 1311.1097, arXiv.org.
    4. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    5. Szafranek, Karol, 2017. "Flattening of the New Keynesian Phillips curve: Evidence for an emerging, small open economy," Economic Modelling, Elsevier, vol. 63(C), pages 334-348.
    6. Chad Fulton & Kirstin Hubrich, 2021. "Forecasting US Inflation in Real Time," Econometrics, MDPI, vol. 9(4), pages 1-20, October.
    7. Christine Garnier & Elmar Mertens & Edward Nelson, 2015. "Trend Inflation in Advanced Economies," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 65-136, September.
    8. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Juan Carlos Berganza & Pedro del Río & Fructuoso Borrallo, 2016. "Determinants and implications of low global inflation rates," Occasional Papers 1608, Banco de España.
    10. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    11. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
    12. Behera, Harendra Kumar & Patra, Michael Debabrata, 2022. "Measuring trend inflation in India," Journal of Asian Economics, Elsevier, vol. 80(C).
    13. Fuhrer, Jeffrey C., 2010. "Inflation Persistence," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 9, pages 423-486, Elsevier.
    14. Christopher G. Gibbs, 2017. "Forecast combination, non-linear dynamics, and the macroeconomy," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 63(3), pages 653-686, March.
    15. Pesaran, M. Hashem & Schuermann, Til & Smith, L. Vanessa, 2009. "Forecasting economic and financial variables with global VARs," International Journal of Forecasting, Elsevier, vol. 25(4), pages 642-675, October.
    16. Gibbs, Christopher G. & Vasnev, Andrey L., 2024. "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1734-1751.
    17. Muellbauer, John & Aron, Janine & Sebudde, Rachel, 2015. "Inflation forecasting models for Uganda: is mobile money relevant?," CEPR Discussion Papers 10739, C.E.P.R. Discussion Papers.
    18. Christiane Baumeister & Lutz Kilian & Thomas K. Lee, 2017. "Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 275-295, March.
    19. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    20. Edward S. Knotek & Saeed Zaman, 2017. "Nowcasting U.S. Headline and Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 931-968, August.

    More about this item

    Keywords

    inflation; forecasting; Phillips curves; transfer functions; judgemental forecasts;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bde:opaper:1703. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ángel Rodríguez. Electronic Dissemination of Information Unit. Research Department. Banco de España (email available below). General contact details of provider: https://edirc.repec.org/data/bdegves.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.